Farm level technical efficiency analysis and production costs in tomato growth: a case study from Turkey

Transcription

1 26 Farm level techncal effcency analyss and producton costs n tomato growth: a case study from Turkey Recebmento dos orgnas: 08/04/2016 Acetação para publcação: 26/10/2016 Orhan Gunduz PhD n Agrcultural Economcs Insttuton: Inonu Unversty, Battalgaz Vocatonal School Address: Battalgaz campus, Malatya, Turkey E-mal: orhangunduz@nonu.edu.tr Sahnde Sl MSc n Agrcultural Economcs Insttuton: Drectorate of Trakya Agrcultural Research Insttute, Agrcultural Economcs Department Address: On the D-100 Hghway Road, Edrne, Turkey E-mal: sahndesl_66@hotmal.com Vedat Ceyhan PhD n Agrcultural Economcs Insttuton: Ondokuz Mays Unversty, Faculty of Agrculture, Department of Agrcultural Economcs Address: Kurupelt campus, Samsun, Turkey E-mal: vceyhan@omu.edu.tr Abstract Techncal effcency and low cost producton are crucal for compettveness of tomato growng farms. Therefore, the am of the present study was to determne producton cost and techncal effcency levels of farms that grow tomato n open felds. Expermental data were obtaned from 50 randomly selected tomato producers. Results showed that unt producton cost of tomato was $0.17 kg -1 and labor costs had the hghest share of total cost. For the estmaton of farm level techncal effcency coeffcents, Stochastc Fronter Analyss method was used. Techncal effcency coeffcents of tomato producng farms vared from 0.35 to 1.00, and the mean was Results of Stochastc Fronter Analyss model showed that nput use n tomato producton could be lowered by 43%. It was found that neffcences of farms were mostly due to preventable techncal factors rather than due to random causes. Farms could work at full effcency by lowerng ther nput use as well as by controllng off farm factors. The study suggested that techncal tranng for tomato growng and extenson programs would be useful to ncrease the effcences of tomato growng farms. Keywords: producton cost, Stochastc fronter analyss, neffcency.

2 27 1. Introducton Farmers make decsons under many rsks and uncertantes. They need enough and correct nformaton to make the best decsons. Some of the nformaton they need durng the decson makng process are about the market condtons and some are about consumer behavor and publc polces. Unt producton cost of crops gve nformaton about market behavors as well as detaled nformaton about nput use effcency of the farm. Collected farm level nformaton allows the calculaton of crop cost and ndcates whether the farm operate n a techncally effcent manner. Fndngs obtaned after analyses of the raw data contrbute to development and mplementaton of sustanable polces. Successful nput use and admnstraton along wth some soco-economcal features of farms and farmheads affect the dfferences among farms for crop costs and effcences. Analyzng these features durng nvestgaton process and ther ntegraton nto emprcal models are crucal to have successful outcomes. Determnaton of producton costs and effcency n tomato farmng could mprove farmers decsons. Tomato s one of the most produced, consumed and marketed crops as human food and has many uses such as drect consumpton as well as a raw materal for food ndustry. Because of ts ecologcal advantages, Turkey s the fourth country n the world for tomato producton (Adanacoglu and Yercan, 2012). Turkey has 311,000 ha of acreage and 12 mllon tons of producton. The country consttutes 7% of the world total for both crtera (FAOSTAT, 2016). Samsun provnce, surveyed n the present study, s the major farmng area n northern Turkey and has a share of 3% n tomato producton of Turkey (Hekmoglu and Altındeger, 2015). Tomato acreage n Samsun has been ncreasng, but tomato yelds have not. The reason for ths has been reported to be the lack of effcent nput use n the regon (Sl, 2013). Despte the mportance of tomato for Samsun provnce and Turkey at large, studes estmatng producton costs and measurng producton effcency are rare. Total producton costs and economc analyss of farms growng tomato were carred out by Engndenz (2007) for ndustral type and table tomato n İzmr provnce, by Esengun et al. (2007) for pole type tomato growng n Tokat provnce, by Cetn and Vardar (2008) for ndustral type tomato grown n Southern Marmara regon and by Keskn et al. (2010) for tomato growng n open felds n some provnces of Turkey. Nevertheless, nput use effcences were not examned n those studes. Besdes, there s lmted number of studes dealng wth producton effcency

3 28 of tomato growng farms. Engndenz and Oztürk Cosar (2013) estmated techncal effcency of tomato growng farms n open felds n İzmr provnce usng Data Envelopment Analyss. Unlke the avalable lterature evaluatng tomato producton cost and producton effcency separately, present study dealt wth these two ssues together. Am of the present study was to estmate costs and effcency of farms growng tomato n open felds n Samsun provnce n an effort to produce data whch mght be helpful for farmers and decson makers. 2. Materals and Methods Expermental data were collected from questonnares conducted n 50 tomato growng farms selected by random samplng n Samsun provnce. Data belongs to year Samplng crteron was sze of land devoted to tomato farmng, and 10% error rate and 90% confdence level was used. For the calculaton of unt producton cost, opportunty cost prncple was employed. Total producton costs were examned under two headngs: varable and fxed costs. Varable costs ncluded costs for seed and seedlng, fertlzer, pestcde, water, machnery rent, repars and mantenance for machnery and nterest of varable costs. Deprecaton, farmland rent, tax, repars and mantenance for buldngs, nterest of fxed costs and general overhead costs consttuted fxed costs. Tomato producton costs were expressed as amounts used per hectare (Kral et al., 1999; Engndenz 2007; Esengün et al. 2007; Cetn and Vardar 2008). For the calculaton of labor costs ncludng famly and outsder labors, duraton of work and labor wages were consdered. Input costs were calculated based on the amount of an nput used and current prce of that specfc nput. Machnery rent costs were calculated based on unt land cultvaton costs n the regon. Fee pad by farmers for rrgaton water was ncluded as water for rrgaton cost. Costs of repars and mantenance for machnery were the share of tomato growng wthn yearly total repar and mantenance costs of the farm. For the nterest of varable costs, 2.8% nterest rate was used (ZB, 2013). For mscellaneous costs n the producton process, 5% of all varable costs were taken. General overhead costs were calculated as 3% of total costs. For buldng and machnery nterest costs, the share for tomato producton was multpled by current nterest rate of 10.05% (CBRT, 2013). Unt cost of tomato producton was calculated by dvson of total producton costs by total amount of producton (Kral et al 1999).

4 29 Stochastc Fronter Analyss (SFA) was used to estmate techncal effcency of tomato growng. SFA approach establshes a functonal relatonshp between dependent varables such as cost, proft and producton, and explanatory varables such as nput and envronmental varables (Berger and Humprey, 1997). It also ncludes an error term n the model. Stochastc effcency fronter approach, a parametrc method, was developed by Agner et al., (1977), Meusen and Broeck (1977) and Battese and Corra (1977) to estmate producton effcency usngy producton functon. Agner et al., (1977) and Meusen and Broeck (1977) x stated that error term ( ) of producton functon n fact conssted of two ndependent elements and formulzed the producton functon as follows: Y x v u (:1,2,,n) (1) v u (2) Y, Producton functon of th farm; x, nput vector of th frm; β, coeffcent. v, random varable that cannot be controlled, has normal dstrbuton N(0, σ 2 v) and s ndependent of u. u s ndependent random varable whch s non-negatve, can partally be controlled and hence lead to techncal neffcency. u can have sem-normal, truncated normal or exponental dstrbuton dependng upon the functon used. Battese and Coell (1995) developed followng model to explan changes n u whch represents techncal neffcency. u z In the formula, z represents specfc features affectng techncal effcency (such as educaton level, age, admnstratve approach), whle δ represents coeffcents. Wth stochastc effcency fronter approach, effcency of a frm could be determned as the rato of observed output to expected output usng equaton 1 (Coell et al. 2005). Based on ths, techncal effcency can be formulated as follows: TE e x v u e x v e u (3) Where u =0 means full effcency. Coell (1995) reported that maxmum lkelhood method s more sutable for the estmaton of producton functons than least squares method. In the present study, effcency of tomato farms were calculated based on Cobb- Douglas type functon wth truncated normal dstrbuton developed by Battese and Coell (1995) usng maxmum lkelhood method. Snce the coeffcents calculated usng Cobb- Douglas functon gves elastcty, they provde easy nterpretatons. Cobb-Douglas type producton functon estmated for the study was as follows:

5 30 lny 4 0 ln x j 1 j v j u u 0 5 m 1 z m m Output n the model was tomato yeld of farms (kg ha -1 ), and nputs were land devoted to tomato farmng (ha), labor (h ha -1 ), number of tomato seedlngs used (ha -1 ) and pestcde costs ($ ha -1 ). Varables used to explan techncal neffcency (u ), on the other hand, were total farm assets, age, experence and educaton level of farm head, and credt use. Stochastc effcency fronter estmatons were made usng FRONTIER 4.1. developed by Coell (2007). Techncally effcent and neffcent farms were compared statstcally. ndependent t test was used for varables wth contnuous dstrbuton and Mann Whtney U test was used for varables determned by rankng or by group level measurement. 3. Fndngs and Dscusson Average sze of farmng household n the study area was fve. Ths was smaller than sx whch was average rural household sze n Samsun provnce but close to average n Turkey (TSI, 2013). Average age of farm head was years and he had an average 6.24 years of educaton. Farms sampled had an average 4.01 ha of farmland, whch was smaller than average farmland area n Turkey (5.9 ha) (TSI, 2013). Vegetables were grown n 52% of farmland, and tomato was grown n 15%. Average land devoted to tomato farmng was 0.62 ha per farm. Tomato producton accounted for 23% of yearly gross producton value of farms. Average tomato yeld of sampled farms was 48,620 kg ha -1. Ths average was lower than average tomato yeld of Samsun provnce (58,530 kg ha -1 ) (FAL, 2012). Tomato producton costs n expermental area were calculated and gven n Table 1. Based on ths, total producton costs were $8, h -1. Of total producton costs, 84.4% was for varable costs and 15.6% was for fxed costs. The hghest share of costs belonged to labor, followed by seed and seedlng and fertlzer costs. Unt cost of tomato was calculated to be $0.17 kg -1 and average ncome from tomato n the same perod was $0.26 kg -1. In some other studes carred out on unt cost of tomato n

6 31 Turkey, Çetn and Vardar (2008) calculated $0.08 kg -1 but Esengün et al. (2007) calculated $0.16 kg -1, whch was smlar to the value calculated n the present study. Table 1: The calculaton of tomato producton cost* Cost tems $ h -1 % Varable costs (A) 6, Seed and seedlng 1, Fertlzers Pestcdes Labor 2, Desel Machnery rent Water for rrgaton Repars and mantenance for machnery Mscellaneous cost (A*5.0%) Interest of varable costs (A*2.8%) Fxed costs (B) 1, Deprecaton Land rent Tax Repars and mantenance for farm buldngs Interest General overhead (A+B)* Total producton cost (A+B) (C) 8, Tomato yeld (kg h -1 ) (D) 48, Unt cost ($ kg -1 ) (D/C) Sellng prce of producer ($ kg -1 ) * $1= 1.85 Turksh Lra n 2012 (CBRT, 2013) Statstcal data from varables of the model establshed to estmate the effcency of farms n the study were gven n Table 2. Average tomato yeld of farms sampled was 48,620 kg ha -1. To produce ths yeld, an average of 0.62 ha land, 1,412 hours of labor, 11,503 seedlng and $507 worth of pestcde were used. Varance parameters were sgnfcant at 1% level. Ths meant that use of a tradtonal producton functon for tomato was not satsfactory and techncal effcency (neffcency) had a sgnfcant effect on tomato yelds of farms. Hgh gamma value (99%) and LR test results showed that u varables (varables whch cannot be negatve and can be partally controlled) were the man cause of neffcency n farms and that ths could cause fluctuatons n yeld levels.

7 32 Table 2: Descrptve statstcs of the varables used n the SFA model Mean Std. Dev. Mn. Max. Dependent Varable Tomato yeld (kg ha -1 ) 48, , , Explanatory varables Tomato land (ha) Labor (h ha -1 ) Seedlng (number ha -1 ) 11, , , , Pestcde ($ ha -1 ) Ineffcency varables Total farm asset ($) 25, , , , Age of farmhead (years) Educaton of farmhead (years) Credt use (If yes 1, otherwse 0) Experence about tomato farm (years) Results of Stochastc Cobb-Douglas model establshed usng maxmum lkelhood method to estmate effcency of tomato growng farms were gven n Table 3. Table 3: Results of the SFA model for tomato farms Varables Coeffcent Std. error t-value Constant *** Ln (tomato land) Ln (Labor) *** Ln (Seedlng) Ln (Pestcde) *** Returns to scale Varance parameters (sgma square) *** (gamma) *** Log-Lkelhood Functon LR test ( ) 4.13*** Ineffcency effects Total farm asset ($) Age of farmhead (years) *** Educaton of farmhead (years) * Credt use (If yes 1, otherwse 0) Experence farmhead (years) ** *, **, *** sgnfcant at 10%, 5% and 1%, respectvely. Coeffcents of varables estmated by Stochastc Effcency Fronter model n the study show the effect of nput use on yeld. Coeffcents whch were not sgnfcant were not dscussed.

8 33 Land devoted to tomato farmng affected tomato yelds negatvely, but the coeffcent was not sgnfcant. Smlarly, number of tomato seedlng used per hectare also had a negatve, but nsgnfcant, effect on tomato yelds. On the other hand, labor nput had a postve and sgnfcant effect on yeld, ndcatng that as the amount of labor ncreased tomato yelds would ncrease. A 1% ncrease n labor nput ncreased tomato yeld by 0.04%. There was a sgnfcant negatve correlaton between pestcde use and tomato yeld varables. Increasng pestcdes costs was negatvely assocated wth amount of tomato producton. One per cent ncrease n pestcde costs meant a 1% decrease n yeld. Results of model estmated for varables that were consdered to be assocated wth neffcency n tomato producton were gven n Table 3. Varables of age of the farm head, hs/her level of educaton, years of experence n tomato producton were sgnfcant, whle total farm assets and credt use varables were not. Increasng age was estmated to cause ncreases n neffcency. Increasng educaton level and experence n tomato producton, on the other hand, were estmated to lower techncal neffcency. Postve or negatve sgns of varables were realzed as expected. These results are n accordance wth those of Bozoğlu and Ceyhan (2007) who estmated the effcency of vegetable farmng n the regon. Bozoğlu and Ceyhan (2007) also reported that ncreasng educatonal level and experence decreased neffcency, whle age ncreased t. Snce Cobb-Douglas type producton functon s expressed fully n a logarthmc manner, coeffcents of the model gve return to the scale. Sum of coeffcents for explanatory varables was less than one (0.52), ndcatng that there were decreasng returns to scale n the farms studed and that nputs were not used n optmum scales. Effcency scores of tomato farms were gven n Table 4. Results ndcated effcences varyng between 0.35 and 1 (average 0.57). As the average of farms, the same producton level could be acheved by 43% less nput use. It could be stated that achevng producton effcency would lower tomato producton costs. The frst study to calculate techncal effcency of tomato producton n Turkey, Engndenz and Öztürk Coşar estmated a techncal effcency of 78% for tomato farms. Bozoğlu and Ceyhan (2007) calculated 82% of producton effcency for vegetable producton n Samsun. These results pont to major effcency problems for tomato farms n the regon and could explan hgh producton costs of the crop.

9 34 Table 4: Descrptves of techncal effcency n the tomato farms Mean techncal effcency score (57%) Standard devaton (13%) Mnmum Maxmum Dstrbuton of techncal effcency coeffcents calculated for farms were gven n Fgure 1. Only one tomato growng farm was found to be fully effcent. Two farms had effcency levels over 90%. These three farms were consdered effcent. Of the remanng farms, 24% had effcences lower than 50%, whle 64% had effcences varyng from 50 to 80%. Nnety sx per cent of the farms nvestgated were techncally neffcency. Bozoğlan and Ceyhan (2007) reported that 89% of the vegetable farms n the regon were neffcency. Engndenz and Öztürk Coşar (2013) reported that 33% of the tomato farms n another regon were effcent and 67% were not. Fgure 1: Dstrbuton of the techncal effcency scores Dfferences between effcent and neffcent tomato farms were nvestgated usng some varables and results were gven n Table 5. As can be seen n the table, there were dfferences between effcent and neffcent tomato farms for some varables. Dfferences for yeld, labor, seedlng, tomato growng land, educaton level of farm head were sgnfcant. Effcent farms had hgher yelds (p<0.10) and acheved these hgh yelds by less seedlng (p<0.05) and labor costs (p<0.10). Techncally effcent farms had hgher level of total farm

10 35 assets, land, farm head s educaton level and experence (Table 5) compared to techncally neffcent farms. Table 5: The dfferences between techncally effcent and neffcent tomato farms Ineffcent farms (n=47) Effcent farms (n=3) Yeld (kg ha -1 )* 47, , Labor (h ha -1 )* 1, , Seedlng (number ha -1 )** 11, , Pestcdes ($ ha -1 ) Total farmland (ha) Tomato land (ha)* Total farm asset 24, , Age of farmhead (year) Educaton of farmhead (year) Expernce of farmhead (year) *, **, *** sgnfcant at 10% and 5%, respectvely. 4. Concluson Fndngs of the present study showed that 16% of the farmland of sampled farms was devoted to tomato growng and average tomato yeld was 48,620 kg ha -1. Unt cost of tomato was $0.17 kg -1. Total producton cost of tomato was $8, ha -1 and 84.40% of the total cost was for varable costs and 15.60% was for fxed costs. The labor cost had the hghest share of costs. Producton effcency of farms n expermental area was 57%. Only 6% of the farms acheved producton effcency and the remanng 94% were neffcent. It was found that farms could acheve producton effcency through decreasng ther nput use by 43% to produce the same amount of tomato. Comparatve analyses revealed that effcent farms were n a better condton for yeld levels, labor use, seedlng use, educatonal level, amount of land and experence than neffcent farms. Unt costs were hgh because of low yelds, poor farmng ablty and use of mproper amounts of nputs. These problems could be allevated through mplementaton of practces that could mprove nput use effcency. Usng modern growng practces, approprate amount and qualty of nput and developng better producer-scentst-extenson specalst relatons could help elmnate effcency problems n tomato farmng.

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